Recent Advances and Applicability of GBS, GWAS, and GS in Soybean

Shingote, Prashant Raghunath; Gotarkar, Dhananjay Narayanrao; Kale, Ravindra Ramrao; Limbalkar, Omkar M.; Wasule, Dhiraj Lalji

doi:10.1002/9781119745686.ch10

Cited by 1 publication

(1 citation statement)

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“…GBS has replaced the previous genotyping markers, i.e., RAPD, ISSR, and SSRs, as it requires less time and labor and is highly cost-effective. GBS technology has been done in legumes like chickpea and soybean ( Shingote et al., 2022 ; Torkamaneh et al., 2021 ; Iquira et al., 2015 ; Bajaj et al., 2016 ; Sudheesh et al., 2021 ; Kujur et al., 2015b ; Verma et al., 2015 ).…”

Section: Genotyping-by-sequencingmentioning

confidence: 99%

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Susmitha

Kumar

Yadav³

et al. 2023

Front. Plant Sci.

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Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

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